updated fuel switch from str to int

energy_demand/enduse_func.py

@@ -1584,12 +1584,14 @@ def generic_fuel_switch(
         if fuel_switch[curr_yr] != 0:
 
             # Get fueltype to switch (old)
-            fueltype_replace_str = fuel_switch['param_info']['fueltype_replace']
-            fueltype_replace_int = tech_related.get_fueltype_int(fueltype_replace_str)
+            #fueltype_replace_str = fuel_switch['param_info']['fueltype_replace']
+            #fueltype_replace_int = tech_related.get_fueltype_int(fueltype_replace_str)
+            fueltype_replace_int = int(fuel_switch['param_info']['fueltype_replace'])
 
             # Get fueltype to switch to (new)
-            fueltype_new_str = fuel_switch['param_info']['fueltype_new']
-            fueltype_new_int = tech_related.get_fueltype_int(fueltype_new_str)
+            #fueltype_new_str = fuel_switch['param_info']['fueltype_new']
+            #fueltype_new_int = tech_related.get_fueltype_int(fueltype_new_str)
+            fueltype_new_int = int(fuel_switch['param_info']['fueltype_new'])
 
             # Value of current year
             fuel_share_switched_cy = fuel_switch[curr_yr]

energy_demand/main.py

@@ -103,8 +103,8 @@ def energy_demand_model(
     user_defined_simulation_end_yr = config['CONFIG']['user_defined_simulation_end_yr']
 
     # Simulated yrs
-    #sim_yrs = [base_yr, user_defined_simulation_end_yr]
-    sim_yrs = [2015, 2025, 2035, 2045, 2050]
+    sim_yrs = [base_yr, user_defined_simulation_end_yr]
+    #sim_yrs = [2015, 2025, 2035, 2045, 2050]
     weather_yr_scenario = 2015   # Default weather year
 
     if len(sys.argv) > 1: #user defined arguments are provide
@@ -125,18 +125,17 @@ def energy_demand_model(
 
     local_scenario = 'pop-baseline16_econ-c16_fuel-c16' #pop-f_econ-c_fuel-c  #pop-b_econ-c_fuel-c
     name_config_path = 'high_electrification'
-    name_config_path = 'low_electrification'
+    name_config_path = 'h_l'
 
-    path_strategy_vars = os.path.join(local_data_path, 'energy_demand', '00_user_defined_variables', 'default')
-    #path_strategy_vars = os.path.join(local_data_path, 'energy_demand', '00_user_defined_variables', 'name_config_path')
+    path_strategy_vars = os.path.join(local_data_path, 'energy_demand', '00_user_defined_variables', 'high_electrification')
+    path_strategy_vars = os.path.join(local_data_path, 'energy_demand', '00_user_defined_variables', name_config_path)
 
     # --- Region definition configuration
     name_region_set = os.path.join(local_data_path, 'energy_demand', 'region_definitions', "lad_2016_uk_simplified.shp")
     name_population_dataset = os.path.join(local_data_path, 'scenarios', 'MISTRAL_pop_gva', 'data', '{}/population__lad.csv'.format(local_scenario))
     name_gva_dataset = os.path.join(local_data_path, 'scenarios', 'MISTRAL_pop_gva', 'data', '{}/gva_per_head__lad_sector.csv'.format(local_scenario))
     name_gva_dataset_per_head = os.path.join(local_data_path, 'scenarios', 'MISTRAL_pop_gva', 'data', '{}/gva_per_head__lad.csv'.format(local_scenario))
 
-    #simulation_name = str(weather_realisation) + "__" + "all_stations"
     simulation_name = "simulation_results"
 
     name_scenario_run = "{}_result_local_{}".format(scenario_name, str(time.ctime()).replace(":", "_").replace(" ", "_"))
@@ -160,7 +159,6 @@ def energy_demand_model(
     # Load data
     # ----------------------------------------------------------------------
     data['scenario_data'] = defaultdict(dict)
-    print(data['paths'])
     data['enduses'], data['sectors'], data['fuels'], lookup_enduses, lookup_sector_enduses = data_loader.load_fuels(data['paths'])
 
     data['regions'] = read_data.get_region_names(name_region_set)
@@ -353,7 +351,10 @@ def energy_demand_model(
     # ------------------------------------------------
     service_switches_raw = pd.read_csv(os.path.join(data['local_paths']['path_strategy_vars'], "switches_service.csv"))
     service_switches = read_data.service_switch(service_switches_raw)
-
+    print(".---------")
+    for i in service_switches:
+        print(i.__dict__)
+    #raise Exception("FF")
     fuel_switches = read_data.read_fuel_switches(os.path.join(data['local_paths']['path_strategy_vars'], "switches_fuel.csv"), data['enduses'], data['assumptions'].fueltypes, data['assumptions'].technologies)
     capacity_switches = read_data.read_capacity_switch(os.path.join(data['local_paths']['path_strategy_vars'], "switches_capacity.csv"))
 
@@ -372,9 +373,10 @@ def energy_demand_model(
         f_reg_norm,
         f_reg_norm_abs,
         crit_all_the_same,
-        fuel_switches,
-        service_switches,
-        capacity_switches)
+        service_switches=service_switches,
+        fuel_switches=fuel_switches,
+        capacity_switches=capacity_switches)
+
     for region in data['regions']:
         regional_vars[region]['annual_tech_diff_params'] = annual_tech_diff_params[region]
 

energy_demand/plotting/fig_3_weather_map.py

@@ -1,17 +1,12 @@
 
 import os
-import logging
-import copy
-import math
 import numpy as np
 import geopandas as gpd
 import pandas as pd
-import palettable
 import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
 from matplotlib.patches import Circle
 from matplotlib.colors import LinearSegmentedColormap
-from collections import defaultdict
 
 from energy_demand.plotting import fig_p2_weather_val, result_mapping
 from energy_demand.basic import basic_functions
@@ -34,8 +29,12 @@ def total_annual_demand(
     """
     if unit == 'GW':
         conversion_factor = 1
-    if unit == 'kWh':
+    elif unit == 'kW':
         conversion_factor = conversions.gwh_to_kwh(gwh=1) #GW to KW
+    elif unit == 'percentage':
+        conversion_factor = 1
+    else:
+        raise Exception("Not defined unit")
 
     df_data_input = df_data_input * conversion_factor
 
@@ -137,12 +136,7 @@ def total_annual_demand(
     # plot with face color attribute
     uk_gdf.plot(ax=ax, facecolor=uk_gdf['bin_color'], edgecolor='black', linewidth=0.5)
 
-    #shp_gdp_merged.plot(column='diff_av_max', scheme='QUANTILES', k=5, cmap='OrRd', linewidth=0.1)
-    #ax = uk_gdf.plot(column='diff_av_max', scheme='QUANTILES', k=5, cmap='OrRd', linewidth=0.1)
-    #uk_gdf[uk_gdf['name'] == 'E06000024'].plot(ax=ax, facecolor='green', edgecolor='black')
-    #uk_gdf[uk_gdf['diff_av_max'] < 0.01].plot(ax=ax, facecolor='blue', edgecolor='black')
 
-    # Get legend patches TODO IMPROVE
     # TODO IMRPVE: MAKE CORRECT ONE FOR NEW PROCESSING
     legend_handles = result_mapping.get_legend_handles(
         bin_values[1:-1],
@@ -175,7 +169,7 @@ def total_annual_demand(
     # --------
     # Labeling
     # --------
-    #plt.title("tttt")
+    plt.title("Peak demand over time")
 
     plt.tight_layout()
     #plt.show()

energy_demand/read_write/read_weather_results.py

@@ -2,19 +2,16 @@
 """
 import os
 import logging
+import numpy as np
 
 from energy_demand.read_write import data_loader, read_data
-from energy_demand.basic import date_prop
-from energy_demand.plotting import plotting_results, result_mapping
-from energy_demand.basic import basic_functions
-from energy_demand.basic import lookup_tables
-from energy_demand.read_write import read_data
 from energy_demand.technologies import tech_related
 
 def read_in_weather_results(
         path_result,
         seasons,
-        model_yeardays_daytype
+        model_yeardays_daytype,
+        fueltype_str
     ):
     """Read and post calculate results from txt files
     and store into container
@@ -30,7 +27,7 @@ def read_in_weather_results(
     """
     logging.info("... Reading in results")
 
-    fueltype_int = tech_related.get_fueltype_int('electricity')
+    fueltype_int = tech_related.get_fueltype_int(fueltype_str)
 
 
     results_container = {}
@@ -53,18 +50,26 @@ def read_in_weather_results(
     print(results_container['ed_reg_peakday'][2015].shape)
     results_container['ed_reg_peakday_peak_hour'] = {}
     results_container['national_peak'] = {}
+    results_container['regional_share_national_peak'] = {}
+
     for year in results_container['ed_reg_peakday']:
 
         # Get peak demand of each region
         results_container['ed_reg_peakday_peak_hour'][year] = results_container['ed_reg_peakday'][year].max(axis=2)
 
         # Get national peak
-        national_demand_per_hour = results_container['ed_reg_peakday'][year].sum(axis=1) #Aggregate houraly across all regions
+        national_demand_per_hour = results_container['ed_reg_peakday'][year].sum(axis=1) #Aggregate hourly across all regions
 
         # Get maximum hour for electricity demand
         max_hour = national_demand_per_hour[fueltype_int].argmax()
 
         results_container['national_peak'][year] = national_demand_per_hour[:, max_hour]
 
+        # Calculate regional share of peak hour to national peak
+        national_peak = results_container['national_peak'][year][fueltype_int]
+        regional_peak = results_container['ed_reg_peakday'][year][fueltype_int][:, max_hour]
+        results_container['regional_share_national_peak'][year] = (100 / national_peak) * regional_peak #1 = 1 %
+
+
     logging.info("... Reading in results finished")
     return results_container

energy_demand/result_processing/weather_result_charts.py

@@ -86,7 +86,8 @@ def main(
         total_regional_demand = pd.DataFrame()
         peak_hour_demand = pd.DataFrame()
         national_peak = pd.DataFrame()
-
+        regional_share_national_peak = pd.DataFrame()
+
         for path_result_folder in paths_folders_result:
             print("path_result_folder: " + str(path_result_folder))
 
@@ -115,7 +116,8 @@ def main(
             results_container = read_weather_results.read_in_weather_results(
                 data['result_paths']['data_results_model_runs'],
                 data['assumptions']['seasons'],
-                data['assumptions']['model_yeardays_daytype'])
+                data['assumptions']['model_yeardays_daytype'],
+                fueltype_str='electricity')
 
             # --Total demand (dataframe with row: realisation, column=region)
             realisation_data = pd.DataFrame(
@@ -139,13 +141,20 @@ def main(
                 columns=data['assumptions']['sim_yrs'])
             national_peak = national_peak.append(realisation_data)
 
+            # --Regional percentage of national peak demand
+            realisation_data = pd.DataFrame(
+                [results_container['regional_share_national_peak'][simulation_yr_to_plot]],
+                columns=data['regions'])
+
+            regional_share_national_peak = regional_share_national_peak.append(realisation_data)
 
         # Add to scenario container
         scenario_result_container.append({
             'scenario_name': scenario_name,
             'total_regional_demand': total_regional_demand,
             'peak_hour_demand': peak_hour_demand,
-            'national_peak': national_peak
+            'national_peak': national_peak,
+            'regional_share_national_peak': regional_share_national_peak
         })
 
     plotting_dict_info = {}
@@ -160,14 +169,14 @@ def main(
         fig_name="scenarios_peak_over_time_{}.pdf".format(fueltype_str),
         result_path=result_path)
 
-
     # ------------------------------
     # Plotting spatial results
     # ------------------------------
     for i in scenario_result_container:
         scenario_name = i['scenario_name']
         total_regional_demand = i['total_regional_demand']
         peak_hour_demand = i['peak_hour_demand']
+        regional_share_national_peak = i['regional_share_national_peak']
 
         print("... plot spatial map of total annual demand")
         field_to_plot = 'std_dev'
@@ -191,10 +200,34 @@ def main(
             pop_data=pop_data,
             simulation_yr_to_plot=simulation_yr_to_plot,
             result_path=result_path,
-            fig_name="{}peak_h_demand_{}_{}.pdf".format(scenario_name, field_to_plot, fueltype_str),
+            fig_name="{}_peak_h_demand_{}_{}.pdf".format(scenario_name, field_to_plot, fueltype_str),
             field_to_plot=field_to_plot,
             unit='GW')
 
+        print("... plot spatial map of percentage of regional peak hour demand")
+        field_to_plot = 'mean'
+        fig_3_weather_map.total_annual_demand(
+            regional_share_national_peak,
+            path_shapefile_input,
+            data['regions'],
+            pop_data=pop_data,
+            simulation_yr_to_plot=simulation_yr_to_plot,
+            result_path=result_path,
+            fig_name="{}_regional_share_national_peak_{}_{}.pdf".format(scenario_name, field_to_plot, fueltype_str),
+            field_to_plot=field_to_plot,
+            unit='percentage')
+
+        field_to_plot = 'std_dev'
+        fig_3_weather_map.total_annual_demand(
+            regional_share_national_peak,
+            path_shapefile_input,
+            data['regions'],
+            pop_data=pop_data,
+            simulation_yr_to_plot=simulation_yr_to_plot,
+            result_path=result_path,
+            fig_name="{}_regional_share_national_peak_{}_{}.pdf".format(scenario_name, field_to_plot, fueltype_str),
+            field_to_plot=field_to_plot,
+            unit='percentage')
 
     print("===================================")
     print("... finished reading and plotting results")

energy_demand/technologies/fuel_service_switch.py

@@ -315,7 +315,7 @@ def autocomplete_switches(
         Criteria wheter regionally specific or not
     regions : list
         Regions
-    f_diffusion : 
+    f_diffusion : TODO
     techs_affected_spatial_f : 
     service_switches_from_capacity : list, default=[]
         Service switches stemming from capacity switches

energy_demand/wrapper_model.py

@@ -238,9 +238,9 @@ def before_simulation(
         f_reg_norm,
         f_reg_norm_abs,
         crit_all_the_same,
-        service_switches,
-        fuel_switches,
-        capacity_switches)
+        service_switches=service_switches,
+        fuel_switches=fuel_switches,
+        capacity_switches=capacity_switches)
 
     for region in data['regions']:
         regional_vars[region]['annual_tech_diff_params'] = annual_tech_diff_params[region]